Estimation of local biogeophysical effects of the continuous logging using a dynamic vegetation model forced by C-based wood harvest

Global continual logging activities have modified the regional landscape and disrupted the energy balance through changing the surface albedo, evapotranspiration and roughness length in both current location (local) and nearby areas through feedback to the atmospheric circulation (non-local). Compared to land use change, less attention has been given to understanding the local biogeophysical effect of the different land use management practices, e.g., wood harvest (logging). Uncertainties from the reconstructed global wood harvest rate forcing data, simplified land heterogeneity and processes representation in the classic big-leaf models largely changed the outcomes. We apply a next generation dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator, FATES), coupled with the land component (ELM) of DOE’s earth system model E3SM to study the local biogeophysical effect and the redistribution of energy after accounting the continuous logging activity on a global scale. In order to account for the uncertainties from forcing data and modeling approaches, we designed 9 parallel experiments with 4 different sets of global wood harvest rates derived from LUH2 reconstructed historical harvest rates combined with 2 different wood harvest methods: area-based harvest and carbon-based harvest. The results highlighted a divergent pattern of the local biogeophysical impact from logging under two dominant stages: regrowth dominant and logging dominant. We found the continuous logging causing up to 5% of the reduction of global canopy coverage and 2% of the increase of albedo. The study also highlighted the uncertainty from the forcing of data sources and modeling approach can lead to a several times difference in the magnitude of local biogeophysical effect.